13 hours ago
Munich, GermanyStaff+
Base Salary
$100k - $200k/yr
Responsibilities
- Structure, filter, and score experimental trajectories for post-training data pipelines.
- Design and implement evaluations and benchmarks for model reasoning, planning, and experimental progress.
- Build reliable agent environments, tool interfaces, observability systems, and replay infrastructure.
- Establish validation and provenance tracking for trajectory and data quality.
- Set ML roadmap priorities across systems, experiments, and hiring.
- Lead the team’s technical direction as it grows.
Requirements
- At least 3 years of experience in machine learning engineering roles delivering production ML systems.
- Strong Python and systems-level programming skills with production software engineering experience building ML infrastructure.
- Hands-on experience with post-training data pipelines, including structuring, filtering, and scoring training data.
- Experience designing and implementing evaluation frameworks and model benchmarks.
- Knowledge of trajectory data, reward modeling, agent decision-making, reinforcement learning, and agent environment design.
- Experience building replay and debugging tools, data validation and provenance systems, observability, tool interfaces, or reinforcement learning training systems.
- Ability to connect research with production, take ownership, and work effectively in an ambiguous environment.
- Experience at a frontier AI lab or in post-training or evaluations at scale is a plus.
Benefits
- Salary range of USD 100,000 to 200,000 annually.
- On-site work in Munich, Germany.
- Visa sponsorship is not available.